Risk information collection device

ABSTRACT

The present invention relates to a risk information collection device which includes: a degree of risk calculator to calculate a degree of risk of a travel state of the vehicle based on travel information of the vehicle, a visually-recognized object specifier to combine visual line information of a driver of the vehicle and object information around the vehicle and specify a name of a visually-recognized object of the driver to set a visually-recognized object candidate, a passenger information acquirer to acquire passenger information including at least words pronounced by a passenger, a risk-inducing factor candidate setter to set a risk-inducing factor candidate for specifying a risk-inducing factor based on the passenger information, and a risk-inducing factor specifier to specify a risk-inducing factor based on the degree of risk, the visually-recognized object candidate, and the risk-inducing factor candidate.

TECHNICAL FIELD

The present invention relates to a risk information collection devicecollecting risk information when a vehicle travels along a road.

BACKGROUND ART

A driver or a passenger feels “near miss”, which is a scene in which thedriver or the passenger feels a sense of danger directly linked to atraffic accident, in some cases when a vehicle travels along a road.There is an example of collecting and organizing information of “nearmiss”, thereby creating a map indicating an area inducing the trafficaccident, and sharing the map for use in preventing the trafficaccident. However, questionnaires are conventionally used for collectingthe information of “near miss”, so that it takes effort and time toorganize and input a result of the questionnaires. Thus, PatentDocuments 1 and 2 propose a system of automatically collecting theinformation of “near miss” from travel information.

PRIOR ART DOCUMENTS Patent Documents

-   Patent Document 1: Japanese Patent Application Laid-Open No.    2003-123185-   Patent Document 2: Japanese Patent Application Laid-Open No.    2007-47914

SUMMARY Problem to be Solved by the Invention

Patent Document 1 discloses a technique of determining whether or not adriver is in danger based on an output from an inter-vehicle distancesensor, a pulsation sensor for the driver, and a sensor such as a voicemicrophone for acquiring a state of a vehicle, for example,automatically determining a type of risk based on a result thereof,specifying a dangerous spot, and reflecting it in map data. However, afactor of inducing a risk cannot be specified by this method.

Patent Document 2 discloses a technique of determining which direction avisual line of a driver is directed to and specifying a dangerous objectdepending on whether or not an object candidate which has caused a stateof near miss is located in the direction of the visual line. However,there may be a plurality of object candidates in this method, so that afactor of inducing a risk cannot be specified.

The present invention therefore has been made to solve problems asdescribed above, and it is an object of the present invention to providea risk information collection device capable of specifying arisk-inducing factor of inducing a risk.

Means to Solve the Problem

A risk information collection device according to the present inventionis a risk information collection device collecting information dangerousto a traveling of the vehicle, including: a travel informationacquisition unit acquiring travel information indicating a travel stateof the vehicle; a degree of risk calculation unit calculating a degreeof risk which is to be an index whether a travel state of the vehicle isin danger based on the travel information acquired in the travelinformation acquisition unit; a visually-recognized object specifyingunit combining visual line information of a driver of the vehicle andobject information around the vehicle and specifying a name of avisually-recognized object of the driver to set a visually-recognizedobject candidate; a passenger information acquisition unit acquiringpassenger information including at least words pronounced by apassenger; a risk-inducing factor candidate setting unit setting arisk-inducing factor candidate for specifying a risk-inducing factorwhich has induced a risk based on the passenger information acquired inthe passenger information acquisition unit; and a risk-inducing factorspecifying unit specifying a risk-inducing factor based on the degree ofrisk calculated in the degree of risk calculation unit, thevisually-recognized object candidate specified in thevisually-recognized object specifying unit, and the risk-inducing factorcandidate being set in the risk-inducing factor candidate setting unit.The risk-inducing factor specifying unit checks a relationship betweenthe visually-recognized object candidate and the risk-inducing factorcandidate when the degree of risk becomes equal to or larger than athreshold value, and specifies the risk-inducing factor candidate as therisk-inducing factor when the visually-recognized object candidate andthe risk-inducing factor candidate are associated with each other.

Effects of the Invention

According to the risk information collection device in the presentinvention, the risk-inducing factor can be specified.

BRIEF DESCRIPTION OF DRAWINGS

[FIG. 1] A function block diagram illustrating a configuration of a riskinformation collection device according to an embodiment 1 of thepresent invention.

[FIG. 2] A flow chart describing an operation of a visually-recognizedobject specifying unit in the risk information collection deviceaccording to the embodiment 1 of the present invention.

[FIG. 3] A flow chart describing operations of a passenger informationacquisition unit and a risk-inducing factor candidate setting unit inthe risk information collection device according to the embodiment 1 ofthe present invention.

[FIG. 4] A flow chart describing operations of a travel informationacquisition unit, a degree of risk calculation unit, and a risk-inducingfactor specifying unit in the risk information collection deviceaccording to the embodiment 1 of the present invention.

[FIG. 5] A drawing describing an example of expressing a degree of riskon a scale of one to ten.

[FIG. 6] A flow chart describing a procedure of checking a relationshipbetween a visually-recognized object candidate group and a risk-inducingfactor candidate group.

[FIG. 7] A drawing describing an example of the relationship between thevisually-recognized object candidate group and the risk-inducing factorcandidate group.

[FIG. 8] A function block diagram illustrating a configuration of a riskinformation collection device according to an embodiment 2 of thepresent invention.

[FIG. 9] A flow chart describing operations of a passenger informationacquisition unit and a risk-inducing factor candidate setting unit inthe risk information collection device according to the embodiment 2 ofthe present invention.

[FIG. 10] A flow chart describing operations of a travel informationacquisition unit, a degree of risk calculation unit, and a risk-inducingfactor specifying unit in the risk information collection deviceaccording to the embodiment 2 of the present invention.

[FIG. 11] A function block diagram illustrating a configuration of arisk information collection device according to an embodiment 3 of thepresent invention.

[FIG. 12] A function block diagram illustrating a configuration of arisk information collection device according to an embodiment 4 of thepresent invention.

[FIG. 13] A function block diagram illustrating a configuration of arisk information collection device according to an embodiment 5 of thepresent invention.

[FIG. 14] A drawing describing an example of risk information.

[FIG. 15] A flow chart describing operations of a travel informationacquisition unit, a degree of risk calculation unit, and a risk-inducingfactor specifying unit in the risk information collection deviceaccording to the embodiment 5 of the present invention.

[FIG. 16] A function block diagram illustrating a modification exampleof the configuration of the risk information collection device accordingto the embodiment 1 of the present invention.

[FIG. 17] A function block diagram illustrating a modification exampleof the configuration of the risk information collection device accordingto the embodiment 2 of the present invention.

[FIG. 18] A function block diagram illustrating a modification exampleof the configuration of the risk information collection device accordingto the embodiment 3 of the present invention.

[FIG. 19] A function block diagram illustrating a configuration of arisk information collection device according to an embodiment 6 of thepresent invention.

[FIG. 20] A flow chart describing operations of a travel informationacquisition unit, a degree of risk calculation unit, and a risk-inducingfactor specifying unit in the risk information collection deviceaccording to the embodiment 6 of the present invention.

[FIG. 21] A function block diagram illustrating a modification exampleof the configuration of the risk information collection device accordingto the embodiment 1 of the present invention.

[FIG. 22] A function block diagram illustrating a modification exampleof the configuration of the risk information collection device accordingto the embodiment 2 of the present invention.

[FIG. 23] A function block diagram illustrating a modification exampleof the configuration of the risk information collection device accordingto the embodiment 3 of the present invention.

[FIG. 24] A function block diagram illustrating a modification exampleof the configuration of the risk information collection device accordingto the embodiment 4 of the present invention.

[FIG. 25] A block diagram illustrating a hardware configuration of therisk information collection device according to the present invention.

[FIG. 26] A block diagram illustrating a hardware configuration of therisk information collection device according to the present invention.

DESCRIPTION OF EMBODIMENT(S) Embodiment 1

FIG. 1 is a function block diagram illustrating a configuration of arisk information collection device 100 according to an embodiment 1 ofthe present invention. As illustrated in FIG. 1, the risk informationcollection device 100 includes a travel information acquisition unit 101acquiring travel information of a vehicle, a degree of risk calculationunit 102, a visually-recognized object specifying unit 103, a passengerinformation acquisition unit 104, a risk-inducing factor candidatesetting unit 106, a risk-inducing factor specifying unit 107, and arisk-inducing factor output unit 108.

The travel information acquisition unit 101 acquires travel informationwhich is a vehicle state amount indicating a travel state of a vehicle,and outputs the travel information to the degree of risk calculationunit 102. The travel information of the vehicle includes a speed, anacceleration, brake information, steering angle information, acceleratorinformation, and engine information, for example. A plurality of sensorsincluded in the vehicle detect the travel information, however, knownmethods are used for the detection, thus the description thereof isomitted.

The degree of risk calculation unit 102 calculates a degree of risk ofthe vehicle based on a degree of change of the travel information beingoutput from the travel information acquisition unit 101 to set riskstate information.

The visually-recognized object specifying unit 103 specifies avisually-recognized object of a driver, using viewpoint information ofthe driver and object information around the vehicle, and outputs thevisually-recognized object as information of a visually-recognizedobject group to the risk-inducing factor specifying unit 107.

The viewpoint information of the driver is acquired using a visual linedetection device. Examples of the visual line detection device include adevice detecting a viewpoint using a camera and infrared light, and awearable device detecting a visual line using muscle potentialinformation around an eyeball, for example. It is also applicable torecognize eyes of the driver, using a camera taking a video in thevehicle and a video analysis function, to specify the viewpoint insteadof the visual line detection device. The viewpoint information includesat least a start point position of the visual line and a direction ofthe visual line. The viewpoint information may additionally includeinformation regarding eyes such as a position of a face or eyes of thedriver, a direction of the face or black eyes, the number of eyewinks, apresence or absence of eye glasses of the driver or a contact lensattached to the driver's eye. Examples of a method of detecting thevisual line include various methods such as a corneal reflection method,and a known optional method can be adopted in the present invention.

The object information around the vehicle is acquired using a cameramounted on the vehicle and a video analysis function. Adoptable as thecamera mounted on the vehicle is a camera including a wide-angle lenscapable of simultaneously taking a video of a front side and lateralside of a subject vehicle and a camera system synthesizing one videofrom videos in a plurality of directions taken by a plurality ofcameras. A vehicle mounting the risk information collection deviceaccording to each embodiment is referred to as a subject vehicle and avehicle other than the subject vehicle is referred to as a non-subjectvehicle hereinafter. A risk information collection device similar tothat in the subject vehicle is also mounted on the other vehicle, andinformation is transmitted and received between the risk informationcollection devices.

A video taken by a camera disposed outside the vehicle such as a roadshoulder may be used. In this case, the video is transmitted from thecamera disposed outside the vehicle to the risk information collectiondevice 100 via a direct communication between the camera disposedoutside the vehicle and the risk information collection device 100 or acloud server, thus the video taken by the camera disposed outside thevehicle can be used. The object information around the vehicle may beacquired using a radar instead of the camera.

The object information includes information of at least a name, arelative position in relation to the subject vehicle, and a size, forexample. The object information may additionally include a color and ashape, for example. For example, information of the object as it is seensuch as “a black minivan, 4 meters in length, relative position(relative distance of 2 m, angle of 60° with respect to the vehicle)”can be included. In this case, the angle with the subject vehicle can beexpressed by a clockwise direction with a traveling direction of thesubject vehicle being set to 0°.

The object information is continuously acquired while the vehicle istraveling. A range of acquiring the object information is calculatedbased on a stopping distance at a time of hard braking on an assumptionof a range with no trouble at the time of hard braking while the vehicleis traveling. For example, the range is set to approximately 60 m indiameter centering on the subject vehicle in a public highway andapproximately 130 m in diameter in an express highway. The range may bechanged depending on a speed.

An example of processing in the visually-recognized object specifyingunit 103 is described herein using a flow chart illustrated in FIG. 2.The risk information collection device 100 starts operating uponswitching on an ignition switch of the vehicle, thus thevisually-recognized object specifying unit 103 also startsvisually-recognized object specifying processing, and thevisually-recognized object specifying unit 103 firstly acquires theviewpoint information of the driver using the visual line detectiondevice, for example (Step S11).

Next, the visually-recognized object specifying unit 103 acquires avideo around the vehicle using an in-vehicle camera taking a videooutside the vehicle, for example, (Step S12), converts a coordinate ofthe viewpoint of the driver acquired in Step S11 into a coordinate ofthe video around the vehicle, and acquires a position coordinate of theviewpoint on the video around the vehicle (Step S13). The conversion ofthe coordinate is processing of converting the position coordinate ofthe viewpoint calculated by the line detection device into a cameracoordinate of the in-vehicle camera used in Step S11. The coordinate isconverted in this manner, thereby being able to map the positioncoordinate of the viewpoint of the driver on the video around thevehicle. When the in-vehicle camera is used, the converted coordinate isuniquely defined in accordance with a position of the camera and aposition of the visual line detection device, thus can be calibrated inadvance.

Next, the object around the position coordinate of the viewpoint of thedriver mapped on the video around the vehicle is detected. A knownmethod such as a method using an image recognition, for example, can beused as a method of detecting the object, and the detected object isspecified as the visually-recognized object (Step S14). The processingof Steps S11 to S14 described above is periodically repeated while theignition switch of the vehicle is ON.

Described as a specific example of the visually-recognized objectspecifying processing described above is a case where the drivervisually recognizes a black minivan located with the relative distanceof 2 m at an angle of 60° with respect to the subject vehicle.

The visual line of the driver with the relative distance of 2 m at theangle 60° is detected in Step S11, the video around the vehicle at thattime is acquired in Step S12, and the viewpoint position (the distanceof 2 m from the subject vehicle and the angle of 60° with respect to thesubject vehicle) is converted on the video around the vehicle in StepS13.

In Step S14, the object information in a viewpoint position (thedistance of 2 m from the subject vehicle and the angle of 60° withrespect to the subject vehicle) on the video around the vehicle isdetected in Step S14, and it is specified that the visually-recognizedobject is the black minivan with a length of 4 m. The information of thespecified visually-recognized object which is “a black minivan, 4 metersin length, relative position (relative distance of 2 m, angle of 60°with respect to the vehicle)” is stored in a predetermined storagedevice together with information of a time of detection. Thepredetermined storage device may be provided in the risk informationcollection device 100, however, it may be provided in the other positionin the vehicle.

The passenger information acquisition unit 104 acquires passengerinformation and outputs it to the risk-inducing factor candidate settingunit 106. The passenger information includes voice information of wordspronounced by the passenger, video information indicating a behavior ofthe passenger pointing to a specific object, and video information of anobject to which the passenger points, for example.

An inside camera taking a video inside the vehicle, an in-vehicle camerataking a video outside the vehicle, a microphone, an accelerometer, anda gyro sensor can be used to acquire the passenger information. Theaccelerometer and the gyro sensor are held by the passenger or disposedon a seat or a seat belt so as to be used for acquiring a direction of aface and body of the passenger and a behavior thereof.

The risk-inducing factor candidate setting unit 106 analyzes thepassenger information being output from the passenger informationacquisition unit 104, and sets a risk-inducing factor candidate group tooutput it to the risk-inducing factor specifying unit 107.

Processing in the passenger information acquisition unit 104 and therisk-inducing factor candidate setting unit 106 is described hereinusing a flow chart illustrated in FIG. 3.

The passenger information is acquired in the passenger informationacquisition unit 104 (Step S21). The passenger information is providedto the risk-inducing factor candidate setting unit 106, and is analyzedin the risk-inducing factor candidate setting unit 106 (Step S22).

Voice analysis is performed on the words of the passenger by voicerecognition as the analysis of the words pronounced by the passenger,and processing of dividing the voice into single words is performed. Forexample, when the voice information of “I would like to go to the parktomorrow.” is acquired, the voice is divided into “tomorrow”, “thepark”, “would like to go”.

As the analysis of the behavior of the passenger, for example, when thepassenger performs a behavior of pointing to a bicycle, the imageanalysis is performed on the video by image recognition to detect apointing operation, and the bicycle to which the finger is directed isspecified as the object. An example of the analysis of the object isdescribed hereinafter.

For example, when the passenger performs a behavior of pointing to abicycle in a case where a direction in which the passenger points isdetected using the video information taken by an inside camera, a shapeof a hand is determined by the image recognition on a video taken by anin-vehicle camera to detect the pointing operation, and a direction inwhich a finger extends is specified to detect the direction in which thepassenger points. Then, a coordinate position of the direction in whichthe passenger points is converted into a coordinate on the video aroundthe vehicle taken by the in-vehicle camera which takes a video outsidethe vehicle, and the bicycle located in that direction is recognized asa bicycle by a pattern matching, for example, and a name thereof isspecified.

Examples of a method of detecting the direction in which the passengerpoints include a method of detecting a direction of a face or adirection of a body using an accelerometer or a gyro sensor. In the casewhere the direction of the body is detected using the accelerometer orthe gyro sensor, applying a state of the passenger facing forward as abasic position, a degree of movement of the body is detected based on anacceleration in orthogonal three axes when the passenger performs abehavior of turning a face or a body in a specific direction. Detectedaccordingly is a degree of an angle of the face or the body with respectto a state where the face or the body faces forward.

According to this method, the direction in which the face or the bodyfaces cannot be accurately specified, thus all of the objects located indirections in which the face or the body is assumed to face arespecified as the visually-recognized object.

The method of detecting the object is similar to the detection method inthe visually-recognized object specifying unit 103, and also applicableis a method of performing image processing on a video taken by anin-vehicle camera or a method of detecting an object by radar.

The risk-inducing factor candidate setting unit 106 sets therisk-inducing factor candidate group based on a result acquired by theanalysis of the passenger information performed in Step S22 (Step S23).For example, when the words of “tomorrow”, “the park”, and “would liketo go” are acquired as a result of the analysis of the voice and “thebicycle” is specified as the object located in the direction in whichthe passenger points in accordance with the behavior of the passenger,“tomorrow”, “the park”, “would like to go”, and “bicycle” are output asthe information of the risk-inducing factor candidate group. Theprocessing of Steps S21 to S23 described above is periodically repeatedwhile the ignition switch of the vehicle is ON.

In this manner, the risk-inducing factor candidate is set by analyzingthe voice of the passenger, thus the risk-inducing factor can bespecified by the information which cannot be acquired from the driver,for example, in a case where the risk-inducing factor occurs in a blindarea for the driver.

The risk-inducing factor candidate is set by analyzing the behavior ofthe passenger, thus the risk-inducing factor can be specified byanalyzing the pointing operation in accordance with the behavior whichcan be performed only by the passenger, for example, even in a casewhere the risk-inducing factor gets away from the vehicle.

The risk-inducing factor specifying unit 107 specifies the risk-inducingfactor using the information of the visually-recognized object groupbeing output from the visually-recognized object specifying unit 103 andthe information of the risk-inducing factor candidate group being outputfrom the risk-inducing factor candidate setting unit 106, and therisk-inducing factor output unit 108 outputs the risk-inducing factorspecified in the risk-inducing factor specifying unit 107 outside.

Next, processing in the travel information acquisition unit 101, thedegree of risk calculation unit 102, and the risk-inducing factorspecifying unit 107 is described using a flow chart illustrated in FIG.4.

The travel information while the vehicle is traveling is acquired in thetravel information acquisition unit 101 (Step S31), and is output to thedegree of risk calculation unit 102. The travel information includes aspeed, an acceleration, brake information, steering angle information,accelerator information, and engine information, for example.

The degree of risk calculation unit 102 calculates a degree of riskwhich is to be an index whether a travel state of the subject vehicle isin danger based on the travel information being output from the travelinformation acquisition unit 101 (Step S32), and outputs the degree ofrisk to the risk-inducing factor specifying unit 107. The degree of riskindicates a degree of risk in the travel state in stages, and when thetravel state enters an abnormal state compared with ordinary times, thedegree of risk increases.

A case of expressing the degree of risk on a scale of one to ten isdescribed using FIG. 5. In the example illustrated in FIG. 5, the degreeof risk is set using speed information, brake information (whether abraking is applied), acceleration information, and steering angleinformation. At this time, the number of engine rotations, engineinformation such as a throttle position, and accelerator information maybe supplementarily used.

In FIG. 5, a state where a hard braking is applied when a speed is low(lower than a legal speed minus 10 km/h), an acceleration is small, anda change of a steering angle is also small falls under a case of “1”where the degree of risk is the lowest. In the meanwhile, a state wherea hard braking is applied when a speed is high (higher than a legalspeed plus 10 km/h), an acceleration is large, and a change of asteering angle is also large falls under a case of “10” where the degreeof risk is the highest. In a case where the degree of risk is “5” to“8”, a speed is equivalent to a legal speed as the speed information,and a state where a speed is equal to or smaller than a legal speed andequal to or larger than a legal speed minus 10 km/h falls under thiscase. The scale described above is one example, thus a combination ofthe travel information is not limited thereto.

In this manner, the degree of risk is set by a numeral value in multiplestages, thus it can be determined whether the travel state of thevehicle is in danger based on the numeral value, and an objectivedetermination can be performed.

The risk-inducing factor specifying unit 107 compares the degree of riskcalculated in the degree of risk calculation unit 102 and apredetermined threshold value (Step S33). When the degree of risk issmaller than the threshold value (no in Step S33), the risk-inducingfactor specifying unit 107 controls the travel information acquisitionunit 101 and the degree of risk calculation unit 102 for purpose ofrepeating the processing subsequent to Step S31. When the degree of riskis equal to or larger than the threshold value (yes in Step S33), therisk-inducing factor specifying unit 107 determines that the travelstate of the subject vehicle is in danger, and the processing makes atransition to Step S34.

The threshold value described above is set based on past travelinformation, however, it may be adjusted for each driver based on pastdata of each driver. It is also applicable to simulate the occurrence ofthe dangerous state several times and set the threshold value based onthe result thereof. For example, in a case where the degree of risk isset on a scale of one to ten and “1” indicates a safe state and “10”indicates dangerous state, when the dangerous state is made tosimulatively occur several times and an average value of the degrees ofrisk is 4, 4 which is the average value may be used as the thresholdvalue.

Next, the risk-inducing factor specifying unit 107 acquires, from thevisually-recognized object specifying unit 103, information of aplurality of visually-recognized objects around a time when the degreeof risk becomes equal to or larger than the threshold value, and sets itto the visually-recognized object candidate group (Step S34). Examplesof the setting of around the time include approximately ten seconds,fifteen seconds, and thirty seconds. This may be set in consideration ofa time until the dangerous state occurs after the driver visuallyrecognizes the risk-inducing factor, that is to say, a time when thedegree of risk becomes equal to or larger than the threshold value and atime until the passenger recognize the risk-inducing factor andtransmits information thereof after the dangerous state occurs. Thereason is that there may be a time lag between a timing at which thedriver and the passenger recognize the risk-inducing factor and a timingat which the driver operates the vehicle to apply a brake, for example.Considered, for example, is a case where the words of “the bicyclerunning out into a road is dangerous” come out after a hard braking. Theinformation of the visually-recognized object group in the timing atwhich the degree of risk becomes equal to or larger than the thresholdvalue, without considering the time lag, may be acquired from thevisually-recognized object specifying unit 103.

Next, the risk-inducing factor specifying unit 107 acquires theplurality of risk-inducing factor candidates around the time when thedegree of risk becomes equal to or larger than the threshold value fromthe risk-inducing factor candidate setting unit 106, and sets theplurality of risk-inducing factor candidates to the risk-inducing factorcandidate group (Step S35). Then, the risk-inducing factor specifyingunit 107 checks a relationship between the visually-recognized objectcandidate group and the risk-inducing factor candidate group (Step S36).In checking the relationship, the visually-recognized object candidategroup and the risk-inducing factor candidate group before and after thetime when the degree of risk becomes equal to or larger than thethreshold value are checked without distinction, and the candidateshaving the highest relationship are set to the risk-inducing factor.

The relationship is quantified as a degree of relationship usinginformation of a size, color, or shape of an object or a meaning of aword. For example, a point of the degree of relationship is set for eachof “a word expressing a degree of risk”, “a word expressing an object”,and “a word expressing a state of an object” with respect to therisk-inducing factor candidate, and the candidate having the highestpoint of the degree of relationship is set to the risk-inducing factor(Step S37).

For example, words expressing a danger such as “dangerous”, “risk” andwords associated with a dangerous behavior such as “could not see”, “ranout into the road”, “ran across the car” are defined as “the wordexpressing the degree of risk”. A noun such a “cat” and “bicycle” isdefined as “the word expressing the object”. A word expressing a shape,size, and color of the object such as “small” and “white” is defined as“the word expressing the state of the object”.

Then, the point is assigned to the visually-recognized object candidateso that the point of “the word expressing the degree of risk” is set toten, the point of “the word expressing the object” is set to five, and“the word expressing the state of the object” is set to one. The pointis assigned for each risk-inducing factor candidate regulated in a setof one sentence. Specific processing in Steps S36 and S37 is describedusing a flow chart illustrated in FIG. 6.

As illustrated in FIG. 6, in checking the relationship between thevisually-recognized object candidate group and the risk-inducing factorcandidate group, it is firstly confirmed whether or not there is “theword expressing the object” in the risk-inducing factor candidateregulated in the set of one sentence (Step S101). Then, there is “theword expressing the object” (yes in Step S101), it is confirmed whetheror not “the word expressing the object” or an equivalent term thereofcoincides with the visually-recognized object candidate (Step S102).When there is no “word expressing the object” (no in Step S101), theprocessing makes a transition to Step S112.

When “the word expressing the object” or the equivalent term thereofcoincides with the visually-recognized object candidate (yes) in StepS102, it is associated with (linked with) the coincidedvisually-recognized object candidate (Step S103), and the processingmakes a transition to Step S104. In the meanwhile, when “the wordexpressing the object” or the equivalent term thereof does not coincidewith the visually-recognized object candidate (no), the point is notassigned to the one sentence of the risk-inducing factor candidate (StepS115), and the processing makes a transition to Step S107.

In Step S112, the visually-recognized object candidate is selected fromthe visually-recognized object candidate group based on the shape, size,and color, and it is confirmed whether or not a property of thevisually-recognized object candidate coincides with that of “the wordexpressing the state of the object”. Then, when the property thereofcoincides with that of “the word expressing the state of the object”(yes in Step S112), it is associated with the coincidedvisually-recognized object candidate (Step S113), and the processingmakes a transition to Step S104. In the meanwhile, when the propertythereof does not coincide with that of “the word expressing the state ofthe object” (no), the point is not assigned to the one sentence of therisk-inducing factor candidate (Step S114), and the processing makes atransition to Step S107.

In Step S104, it is confirmed whether or not there is “the wordexpressing the degree of risk” in the risk-inducing factor candidate.When there is “the word expressing the degree of risk” (yes in StepS104), the processing makes a transition to Step S108 to add ten pointsto the degree of relationship of the associated visually-recognizedobject candidate, and the processing makes a transition to Step S105. Inthe meanwhile, when there is no “word expressing the degree of risk” (noin Step S104), the processing makes a transition to Step S105.

In Step S105, it is confirmed whether or not there is “the wordexpressing the object” in the risk-inducing factor candidate. When thereis “the word expressing the object” (yes in Step S105), the processingmakes a transition to Step S109 to add five points to the degree ofrelationship of the associated visually-recognized object candidate, andthe processing makes a transition to Step S106. In the meanwhile, whenthere is no “word expressing the object” (no in Step S105), theprocessing makes a transition to Step S106.

In Step S106, it is confirmed whether or not there is “the wordexpressing the state of the object” in the risk-inducing factorcandidate. When there is “the word expressing the state of the object”(yes in Step S106), the processing makes a transition to Step S110 toadd one point to the degree of relationship of the associatedvisually-recognized object candidate, and the processing makes atransition to Step S107. In the meanwhile, when there is no “wordexpressing the state of the object” (no), the processing makes atransition to Step S107.

In Step S107, it is confirmed whether or not there is an unprocessedrisk-inducing factor candidate on which checking processing has not beenperformed. When there is the unprocessed risk-inducing factor candidate(yes in Step S107), the processing subsequent to Step S101 is repeated,and there is no unprocessed risk-inducing factor candidate (no in StepS107), the processing makes a transition to Step S111.

The above description is based on an assumption that there are theplurality of risk-inducing factor candidates. However, when there isonly one risk-inducing factor candidate and the risk-inducing factorcandidate is associated with the visually-recognized object candidate inStep S103 or Step S113, it is confirmed that the risk-inducing factorcandidate and the visually-recognized object candidate are associatedwith each other only by that processing, and the risk-inducing factorcan be specified.

Steps S104 to S111 can be considered as the necessary steps when thereare the plurality of risk-inducing factor candidates, and can correspondto a case where there are the plurality of risk-inducing factorcandidates.

FIG. 7 is a drawing illustrating an example of calculating the degree ofrelationship according to the checking processing described above. Asillustrated in FIG. 7, described is a method of calculating the degreeof relationship in a case where there is “cat” in thevisually-recognized object candidate group and there is a risk-inducingfactor candidate A of “cat is”, “too small”, and “could not see”. To thevisually-recognized object candidate of “cat”, five points are added by“cat is” which is “the word expressing the object”, one point is addedby “too small” which is “the word expressing the state of the object”,and ten points are added by “could not see” which is “the wordexpressing the degree of risk”. That is to say, the degree ofrelationship between the risk-inducing factor candidate A and “cat”included in the visually-recognized object candidate is sixteen points.

A risk-inducing factor candidate C of “too small” and “could not see”has eleven points made up of one point added to “too small” and tenpoints added to “could not see”. In this case, “small” used as a wordfor replacing “cat” is included, thus the risk-inducing factor candidateC is associated with “cat” included in the visually-recognized objectcandidate.

A risk-inducing factor candidate D of “small” and “cute” has one pointmade up of one point added to “small”. In this case, “small” used as aword for replacing “cat” is included, thus the risk-inducing factorcandidate D is associated with “cat” included in the visually-recognizedobject candidate.

Accordingly, the degree of relationship between the risk-inducing factorcandidates A, C, and D and “cat” included in the visually-recognizedobject candidate is twenty-eight points in total.

A risk-inducing factor candidate B of “tomorrow”, “by car”, and “go out”has five points made up of five points added to “by car” which is “theword expressing the object”. The visually-recognized object candidategroup includes “car”, thus the degree of relationship between therisk-inducing factor candidate B and “car” included in thevisually-recognized object candidate is five points. “Bicycle” includedin the visually-recognized object candidate does not have a wordrelating to the risk-inducing factor candidate group, thus the degree ofrelationship is zero point.

In Step S111, the risk-inducing factor candidate having the highestpoint of degree of relationship with the visually-recognized candidateis specified as the risk-inducing factor. For example, the risk-inducingfactor candidate A has the highest point of degree of relationship with“cat” included in the visually-recognized candidate in the risk-inducingfactor candidates A, C, and D associated with “cat” included in thevisually-recognized candidate. “Cat” which is “the word expressing theobject” is specified as the risk-inducing factor, and the risk-inducingfactor being output by the risk-inducing factor output unit 108 iseventually determined to be “cat”.

The degree of relationship between the visually-recognized objectcandidate group and the risk-inducing factor candidate group may besequentially calculated as described above, however, also applicable isa method of setting a combination of the visually-recognized object andthe risk-inducing factor having the high degree of relationship inadvance and selecting a combination coinciding with the presetcombination. For example, as the method of setting the combination, whenthe visually-recognized object is “cat”, “cat” is combined with a wordexpressing a size, color, or shape such as “small”, “downy”, “hair”,“ears”, “white”, “black”, and “tail” each used as a word for replacing“cat”. When the visually-recognized object is “car”, “car” is combinedwith a word expressing the size, color, and shape such as “black”,“large”, “wagon”, “hood”, “small”, “opened”, and “broom-broom”. Forexample, when “cat” is included in the visually-recognized objectcandidate group and there is the word of “hair” in the risk-inducingfactor candidate, “hair”, that is to say, “cat” having the highrelationship is specified as the risk-inducing factor.

Herein, a destination of information being output from the risk-inducingfactor output unit 108 is mainly a driver and a passenger of anon-subject vehicle, and a smartphone and a car navigation systemmounted on the non-subject vehicle to which the information istransmitted via a cloud server, for example. It is also applicable thatthe destination of the information is a system included in the subjectvehicle and an output result is stored in a predetermined storagedevice.

When the information is provided to the driver of the non-subjectvehicle, for example, as the destination of the information, a displayof alerting the driver's attention such as “bicycle often runs out intothe road” and “beware the dog” is displayed on a screen of a carnavigation system, for example.

When the destination of the information is the subject vehicle, “today'snear miss information (a near miss caused by a cat)” is displayed on ascreen of a car navigation system or a smartphone of the driver at theend of driving the vehicle. The above configuration enables the driverto look back his/her driving, and can be used for improving a drivingtechnique.

The information is output to the cloud server, for example, to combinethe information with information of the plurality of other vehicles,thus “near miss” information can be analyzed in detail, the number of“near miss” can be reduced by removing factors and performing a driveguidance on the driver, and a traffic accident can be prevented.

As described above, according to the risk information collection device100 of the embodiment 1 of the present invention, the object inducingthe risk can be specifically specified. Thus, the factor which hasinduced the risk can be specified in addition to the function ofspecifying the dangerous spot, thus the user can be provided with theinformation indicating the cause of the risk.

Embodiment 2

FIG. 8 is a function block diagram illustrating a configuration of arisk information collection device 200 according to an embodiment 2 ofthe present invention. The risk information collection device 200illustrated in FIG. 8 includes a risk association information database105 in addition to the configuration of the risk information collectiondevice 100 according to the embodiment 1 illustrated in FIG. 1. In FIG.8, the same reference numerals will be assigned to the sameconfiguration as the risk information collection device 100 describedusing FIG. 1, and a duplicate description is omitted.

The risk association information database 105 stores informationassociated with a risk for each passenger information. That is to say,when the passenger information is the word pronounced by the passenger,the word associated with the risk is stored. Examples of the wordassociated with the risk include “could not see”, “dangerous”, and“barely okay”. When the passenger information is the information of theobject indicated by the passenger, examples of the object associatedwith the risk include “bicycle”, “pedestrian”, “car”, “telephone pole”,and “dog”.

In the risk-inducing factor candidate setting unit 106, processing ofanalyzing the passenger information and setting the risk-inducing factorcandidate group is the same as that in the embodiment 1, however, addedis processing of checking an analysis result of the passengerinformation and information associated with the risk stored in the riskassociation information database 105.

Processing in the passenger information acquisition unit 104 and therisk-inducing factor candidate setting unit 106 is described hereinusing a flow chart illustrated in FIG. 9.

The passenger information is acquired in the passenger informationacquisition unit 104 (Step S41). The passenger information is providedto the risk-inducing factor candidate setting unit 106, and is analyzedin the risk-inducing factor candidate setting unit 106 (Step S42).

Processing of dividing the voice into single words is performed usingvoice recognition as the analysis of the voice pronounced by thepassenger. The object located in a direction indicated by the passengeris specified using information of a behavior of the passenger or adirection of the passenger's face or body as the analysis of the objectindicated by the passenger.

Then, checked is the analysis result of the passenger informationacquired in Step S42 against the information associated with the riskstored in the risk association information database 105 (Step S43). InStep S43, when it is analyzed in Step S42 that information of “bicycle”,“could not see”, and “tomorrow” is included in the words pronounced bythe passenger, for example, and words of “could not see” are stored inthe risk association information database 105, it is determined that thedegree of relationship of the words of “could not see” is high. Then,the words of “could not see” and the words of “bicycle” and “tomorrow”pronounced before and after the words of “could not see” are set to therisk-inducing factor candidate (Step S44).

The risk association information database 105 includes the words of“could not see” which often occurs in a conversation between passengersin the vehicle at the time of the dangerous state, for example, thususeless information is prevented from being included as therisk-inducing factor candidate, and the information can be refined. Forexample, in a case where the analysis result in Step S42 includesinformation of “a bicycle ran out into the road”, “I want to have acat”, “I am hungry”, for example, when the analysis result is notchecked against the risk association information database 105, the wordsof “bicycle”, “ran out into the road”, “cat”, “want to have”, and“hungry” are set to the risk-inducing factor candidate. However, therisk association information database 105 includes the words of “ran outinto the road”, it is possible to determine that the degree ofrelationship of the words of “ran out into the road” is high, and setonly the words of “bicycle”, “ran out into the road”, and “cat” to therisk-inducing factor candidate group. The risk association informationdatabase 105 stores an expected word in a predetermined storage devicein advance as a database. It is also applicable to add information ofthe risk-inducing factor candidate acquired in the risk-inducing factorcandidate setting unit 106 in accordance with the operation of the riskinformation collection device 200 to the database, thereby enhancing thedatabase.

The risk-inducing factor specifying unit 107 specifies the risk-inducingfactor using the information of the visually-recognized object groupbeing output from the visually-recognized object specifying unit 103 andthe information of the risk-inducing factor candidate group being outputfrom the risk-inducing factor candidate setting unit 106. Therisk-inducing factor output unit 108 outputs the risk-inducing factorspecified in the risk-inducing factor specifying unit 107 outside.

Next, processing in the travel information acquisition unit 101, thedegree of risk calculation unit 102, and the risk-inducing factorspecifying unit 107 is described using a flow chart illustrated in FIG.10.

The travel information while the vehicle is traveling is acquired in thetravel information acquisition unit 101 (Step S51), and is output to thedegree of risk calculation unit 102.

The degree of risk calculation unit 102 calculates a degree of riskwhich is to be an index whether a travel state of the subject vehicle isin danger based on the travel information being output from the travelinformation acquisition unit 101 (Step S52), and outputs the degree ofrisk to the risk-inducing factor specifying unit 107.

The risk-inducing factor specifying unit 107 compares the degree of riskcalculated in the degree of risk calculation unit 102 and apredetermined threshold value (Step S53). When the degree of risk issmaller than the threshold value (no in Step S53), the risk-inducingfactor specifying unit 107 controls the travel information acquisitionunit 101 and the degree of risk calculation unit 102 for purpose ofrepeating the processing subsequent to Step S31. When the degree of riskis equal to or larger than the threshold value (yes in Step S53), therisk-inducing factor specifying unit 107 determines that the travelstate of the subject vehicle is in danger, and the processing makes atransition to Step S54. The method of setting the threshold value is thesame as that in the embodiment 1.

Next, the risk-inducing factor specifying unit 107 acquires, from thevisually-recognized object specifying unit 103, information of avisually-recognized object group around a time when the degree of riskbecomes equal to or larger than the threshold value (Step S54).

Next, the risk-inducing factor specifying unit 107 acquires all therisk-inducing factor candidates acquired from the risk-inducing factorcandidate setting unit 106 before this processing is started after thevehicle is activated (Step S55). In this manner, all the risk-inducingfactor candidates are acquired without a refinement in accordance withtime, thus the visually-recognized object candidate and therisk-inducing factor candidate can be linked with each other even in astate where a point of time when the dangerous state occurs and a pointof time when the information regarding the risk-inducing factor isacquired from the passenger are away from each other, that is to say,even when there is a time gap therebetween. For example, thevisually-recognized object candidate and the risk-inducing factorcandidate can be linked with each other even in a case where thepassenger says that “the cat running out into the road was dangerous atthat time”, for example, regarding the dangerous state when thetraveling is finished sometime after the dangerous state has occurred.

Next, the risk-inducing factor specifying unit 107 checks a relationshipbetween the visually-recognized object candidate group and therisk-inducing factor candidate group (Step S56). In checking therelationship, the candidate having the highest relationship is set tothe risk-inducing factor.

The relationship is quantified as a degree of relationship usinginformation of a size, color, or shape of an object or a meaning of aword. For example, a point of the degree of relationship is set for eachof “a word expressing a degree of risk”, “a word expressing an object”,and “a word expressing a state of an object” with respect to therisk-inducing factor candidate, and the candidate having the highestpoint of the degree of relationship is set to the risk-inducing factor(Step S57).

For example, words expressing a danger such as “dangerous”, “risk” andwords associated with a dangerous behavior such as “could not see”, “ranout into the road”, “ran across the car” are defined as “the wordexpressing the degree of risk”. A noun such a “cat” and “bicycle” isdefined as “the word expressing the object”. A word expressing a shape,size, and color of the object such as “small” and “white” is defined as“the word expressing the state of the object”.

Then, the point is assigned to the visually-recognized object candidateso that the point of “the word expressing the degree of risk” is set toten, the point of “the word expressing the object” is set to five, and“the word expressing the state of the object” is set to one. The pointis assigned for each set of one sentence of the risk-inducing factorcandidate. Specific processing in Steps S56 and S57 is the same as thatdescribed using FIG. 6.

The risk-inducing factor output unit 108 outputs the risk-inducingfactor specified in Step S57 outside. A destination of information beingoutput from the risk-inducing factor output unit 108 is mainly a driverand a passenger of a non-subject vehicle, and a smartphone and a carnavigation system mounted on the non-subject vehicle to which theinformation is transmitted via a cloud server, for example. It is alsoapplicable that the destination of the information is a system includedin the subject vehicle and an output result is stored in a predeterminedstorage device.

As described above, according to the risk information collection device200 of the embodiment 2 of the present invention, the object inducingthe risk can be specifically specified. Thus, the factor which hasinduced the risk can be specified in addition to the function ofspecifying the dangerous spot, thus the user can be provided with theinformation indicating the cause of the risk. The risk informationcollection device 200 includes the risk association information database105. The analysis result of the passenger information is checked againstthe information associated with the risk stored in the risk associationinformation database 105, thus useless information is prevented frombeing included as the risk-inducing factor candidate, and theinformation can be refined. Accordingly, an amount of time spent on theacquisition of the risk-inducing factor candidate can be reduced.

Embodiment 3

FIG. 11 is a function block diagram illustrating a configuration of arisk information collection device 300 according to an embodiment 3 ofthe present invention. The risk information collection device 300illustrated in FIG. 11 includes a position information data acquisitionunit 109 acquiring a position of the subject vehicle in addition to theconfiguration of the risk information collection device 200 according tothe embodiment 2 illustrated in FIG. 8. In FIG. 11, the same referencenumerals will be assigned to the same configuration as the riskinformation collection device 200 described using FIG. 8, and aduplicate description is omitted. Also applicable is a configurationincluding the position information data acquisition unit 109 acquiringthe position of the subject vehicle in addition to the configuration ofthe risk information collection device 100 according to the embodiment1.

In the position information data acquisition unit 109, a positioningsystem such as global positioning system (GPS) is used for acquiring theposition of the subject vehicle.

As illustrated in FIG. 11, position information of the subject vehicleacquired in the position information data acquisition unit 109 is outputto the risk-inducing factor output unit 108. The risk-inducing factoroutput unit 108 outputs the risk-inducing factor specified in therisk-inducing factor specifying unit 107 and the position information ofthe subject vehicle at a point of time when the dangerous state hasoccurred. The point of time when the dangerous state has occurs is atime when the degree of risk calculated in the degree of riskcalculation unit 102 becomes equal to or larger than the thresholdvalue.

A destination of information being output from the risk-inducing factoroutput unit 108 is mainly a driver and a passenger of a non-subjectvehicle, and a smartphone and a car navigation system mounted on thenon-subject vehicle to which the information is transmitted via a cloudserver, for example. It is also applicable that the destination of theinformation is a system included in the subject vehicle and an outputresult is stored in a predetermined storage device.

As described above, according to the risk information collection device300 of the embodiment 3 of the present invention, the object inducingthe risk can be specifically specified. Thus, the factor which hasinduced the risk can be specified in addition to the function ofspecifying the dangerous spot, thus the user can be provided with theinformation indicating the cause of the risk. The risk informationcollection device 300 includes the risk association information database105, and checks the analysis result of the passenger information againstthe information associated with the risk stored in the risk associationinformation database 105. Accordingly, useless information is preventedfrom being included as the risk-inducing factor candidate, and theinformation can be refined. Thus, an amount of time spent on theacquisition of the risk-inducing factor candidate can be reduced. Therisk information collection device 300 outputs the position informationof the subject vehicle at the point of time when the dangerous state hasoccurred, thus the more specific information regarding the dangerousstate can be provided. For example, measures can be easily taken so thatthat the non-subject vehicle getting close to a position where thedangerous state, which the subject vehicle encountered, has occurredavoids the risk, for example.

Embodiment 4

FIG. 12 is a function block diagram illustrating a configuration of arisk information collection device 400 according to an embodiment 4 ofthe present invention. The risk information collection device 400illustrated in FIG. 12 includes a surround information collection unit110 collecting surround information of a position where the dangerousstate has occurred in addition to the configuration of the riskinformation collection device 300 according to the embodiment 3illustrated in FIG. 11. In FIG. 12, the same reference numerals will beassigned to the same configuration as the risk information collectiondevice 300 described using FIG. 11, and a duplicate description isomitted. Also applicable is a configuration including the positioninformation data acquisition unit 109 acquiring the position of thesubject vehicle in addition to the configuration of the risk informationcollection device 100 according to the embodiment 1 or the configurationof the risk information collection device 200 according to theembodiment 2.

As illustrated in FIG. 12, the surround information, collected in thesurround information collection unit 110, in a position where thedangerous state has occurred is output to the risk-inducing factoroutput unit 108. The risk-inducing factor output unit 108 outputs therisk-inducing factor specified in the risk-inducing factor specifyingunit 107, the position information of the subject vehicle at the pointof time when the dangerous state has occurred, and the surroundinformation, collected in the surround information collection unit 110,in a position where the dangerous state has occurred.

The surround information is information such as a time, weather, acongestion degree of vehicle, and a congestion degree of people acquiredfrom surrounding area of the subject vehicle. With regard to thecongestion degree of vehicle, a range of the surrounding area isspecified within a radius centering on the subject vehicle, for example,and the radius may be selected from 1 km, 3 km, and 5 km. With regard tothe congestion degree of people, a range of the surrounding area isspecified within a radius centering on the subject vehicle, for example,and the radius may be selected from 100 m, 300 m, and 500 m.

The congestion degree of people in the surround information can becollected from a cloud service using Internet. For example, informationof a position where the subject vehicle is located can be collected froma homepage publishing information of the congestion degree which anInternet service company providing a free application acquires from ausage situation of the free application. The congestion degree ofvehicle can also be collected from a homepage of a traffic jaminformation service on Internet. The time and the weather, for example,can be collected from a homepage on Internet.

The congestion degree of people may be obtained by recognizing a humanby image recognition and dividing the number of people in apredetermined range by an area of the predetermined range, using a videotaken by a camera mounted on the subject vehicle to take a video outsidethe vehicle or a camera, such as a street camera near the subjectvehicle, disposed outside the vehicle. When the video taken by thecamera disposed outside the vehicle is used, the video is transmittedfrom the camera disposed outside the vehicle to the risk informationcollection device 400 via a direct communication between the cameradisposed outside the vehicle and the risk information collection device400 or a cloud server, thus the video taken by the camera disposedoutside the vehicle can be used.

A destination of information being output from the risk-inducing factoroutput unit 108 is mainly a driver and a passenger of a non-subjectvehicle, and a smartphone and a car navigation system mounted on thenon-subject vehicle to which the information is transmitted via a cloudserver, for example. It is also applicable that the destination of theinformation is a system included in the subject vehicle and an outputresult is stored in a predetermined storage device.

As described above, according to the risk information collection device400 of the embodiment 4 of the present invention, the object inducingthe risk can be specifically specified. Thus, the factor which hasinduced the risk can be specified in addition to the function ofspecifying the dangerous spot, thus the user can be provided with theinformation indicating the cause of the risk. The risk informationcollection device 400 includes the risk association information database105, and checks the analysis result of the passenger information againstthe information associated with the risk stored in the risk associationinformation database 105. Accordingly, useless information is preventedfrom being included as the risk-inducing factor candidate, and theinformation can be refined, thus an amount of time spent on theacquisition of the risk-inducing factor candidate can be reduced. Therisk information collection device 400 outputs the position informationof the subject vehicle at the point of time when the dangerous state hasoccurred and the surround information of the position where thedangerous state has occurred, thus the more specific informationregarding the dangerous state can be provided. Accordingly, measures canbe easily taken so that that the non-subject vehicle getting close to aposition where the dangerous state, which the subject vehicleencountered, has occurred avoids the risk, for example, and measures ofavoiding a traffic jam can be easily taken based on the surroundinformation, for example.

Embodiment 5

FIG. 13 is a function block diagram illustrating a configuration of arisk information collection device 500 according to an embodiment 5 ofthe present invention. The risk information collection device 500illustrated in FIG. 13 includes a risk information database 111 in whicha risk-inducing factor which occurred in past and position informationare registered in addition to the configuration of the risk informationcollection device 400 according to the embodiment 4 illustrated in FIG.12. In FIG. 15, the same reference numerals will be assigned to the sameconfiguration as the risk information collection device 400 describedusing FIG. 12, and a duplicate description is omitted.

As illustrated in FIG. 13, past risk information accumulated in the riskinformation database 111 is output to the risk-inducing factorspecifying unit 107. The risk-inducing factor specifying unit 107specifies the risk-inducing factor using the information of thevisually-recognized object group being output from thevisually-recognized object specifying unit 103, the information of therisk-inducing factor candidate group being output from the risk-inducingfactor candidate setting unit 106, the current position informationbeing output from the position information data acquisition unit 109,and the past risk information being output from the risk informationdatabase 111, and outputs the risk-inducing factor to the risk-inducingfactor output unit 108.

The risk information database 111 records a past risk-inducing factorand position information in a position thereof and time information. Therisk information database 111 may store a vehicle ID. The vehicle ID isidentification information for identifying a vehicle, and amanufacturing number may be applied to the vehicle ID, for example. FIG.14 illustrates an example of the past risk information recorded in therisk information database 111.

The risk-inducing factor and the position information are input from therisk-inducing factor output unit 108 to the risk information database111, and stored in the risk information database 111. Risk informationaccumulated in past may be stored in the risk-inducing informationdatabase 111 at a time of shipping the risk information collectiondevice 500.

In FIG. 14, the cat and the bicycle are recorded as the risk-inducingfactor, and the vehicle ID of the vehicle which has encountered the catand the bicycle and latitude and longitude information as the positioninformation in a position where the vehicle has encountered the cat andthe bicycle are recorded with time information of a time of theencounter.

FIG. 15 is a flow chart describing an operation of the risk informationcollection device 500. Processing in Steps S61 to S65 is the same as theprocessing in Steps S51 to S55 in the flow chart illustrated in FIG. 10,and processing of calculating the degree of relationship between thevisually-recognized object candidate group and the risk-inducing factorcandidate in the risk-inducing factor specifying unit 107 is the same asthat in the embodiment 4. However, added is weighting processing in StepS67 for adding a numeral value to the degree of relationship based onthe risk-inducing factor and the position information which are paststatistical information stored in the risk information database 111after the degree of relationship is calculated as the numeral value inStep S66.

As illustrated in FIG. 14, the risk information database 111 records thepast risk-inducing factor and the position information in the positionthereof. The risk-inducing factor specifying unit 107 checks therelationship between the visually-recognized object candidate group andthe risk-inducing factor candidate group from the information of thevisually-recognized object group being output from thevisually-recognized object specifying unit 103 and the information ofthe risk-inducing factor candidate group being output from therisk-inducing factor candidate setting unit 106 (Step S66).

After checking the relationship, the risk-inducing factor specifyingunit 107 searches the information stored in the risk informationdatabase 111 to confirm, based on the position information being outputfrom the position information data acquisition unit 109, whether or notthere are position information at a point of time when the degree ofrisk calculated in the degree of risk calculation unit 102 becomes equalto or larger than a threshold value and a risk-inducing factorassociated with a surrounding area of a position indicated by theposition information. The range of the surrounding area is specifiedwithin a radius centering on the subject vehicle, for example, and theradius may be selected from 300 m, 500 m, and 1 km.

When there is the corresponding risk-inducing factor, the risk-inducingfactor specifying unit 107 acquires information thereof. Therisk-inducing factor specifying unit 107 checks the risk-inducing factorcandidate against the past risk-inducing factor which has been acquired,and when the risk-inducing factor candidate coincides with the pastrisk-inducing factor, a point is added to the degree of relationship ofthe coinciding risk-inducing factor candidate (Step S67). For example,in a case where there is the word of “cat” in the past risk-inducingfactor which has been acquired, the point is added to the degree ofrelationship of “cat” in the risk-inducing factor candidate when thereis “cat” in the risk-inducing factor candidate.

The risk-inducing factor specifying unit 107 determines therisk-inducing factor candidate having the highest point of the degree ofrelationship, including the weighted degree of relationship, to be therisk-inducing factor (Step S68).

As described above, according to the risk information collection device500 of the embodiment 5 of the present invention, the object inducingthe risk can be specifically specified. Thus, the factor which hasinduced the risk can be specified in addition to the function ofspecifying the dangerous spot, thus the user can be provided with theinformation indicating the cause of the risk. The risk informationcollection device 500 includes the risk information database 111, anduses the past risk-inducing factor based on the position information.Accordingly, the information of the risk-inducing factor, such asgeographical characteristics, which tends to occur in a specificposition can be added to the checking of the relationship, thus accuracyfor specifying the risk-inducing factor can be increased.

MODIFICATION EXAMPLE

The position information data acquisition unit 109 and the riskinformation database 111 are added to the risk information collectiondevice 100 in the embodiment 1 or the risk information collection device200 in the embodiment 2, thereby being able to increase the accuracy forspecifying the risk-inducing factor.

That is to say, a risk information collection device 100A illustrated inFIG. 16 has a configuration that not only the information of thevisually-recognized object group being output from thevisually-recognized object specifying unit 103 and the information ofthe risk-inducing factor candidate group being output from therisk-inducing factor candidate setting unit 106 but also currentposition information being output from the position information dataacquisition unit 109 and past risk information being output from therisk information database 111 are input to the risk-inducing factorspecifying unit 107.

A risk information collection device 200A illustrated in FIG. 17 has aconfiguration that not only the information of the visually-recognizedobject group being output from the visually-recognized object specifyingunit 103 and the information of the risk-inducing factor candidate groupbeing output from the risk-inducing factor candidate setting unit 106but also current position information being output from the positioninformation data acquisition unit 109 and past risk information beingoutput from the risk information database 111 are input to therisk-inducing factor specifying unit 107.

The risk information database 111 is added to the risk informationcollection device 300 in the embodiment 3, thereby being able toincrease the accuracy for specifying the risk-inducing factor.

That is to say, a risk information collection device 300A illustrated inFIG. 18 has a configuration that the position information of the subjectvehicle acquired in the position information data acquisition unit 109is input not to the risk-inducing factor output unit 108 but to therisk-inducing factor specifying unit 107, and not only the informationof the visually-recognized object group being output from thevisually-recognized object specifying unit 103 and the information ofthe risk-inducing factor candidate group being output from therisk-inducing factor candidate setting unit 106 but also currentposition information being output from the position information dataacquisition unit 109 and past risk information being output from therisk information database 111 are input to the risk-inducing factorspecifying unit 107.

In FIG. 16, FIG. 17, and FIG. 18, the same reference numerals will beassigned to the same configuration as the risk information collectiondevices 100, 200, and 300 described using FIG. 1, FIG. 8, and FIG. 11,and a duplicate description is omitted.

Embodiment 6

FIG. 19 is a function block diagram illustrating a configuration of arisk information collection device 600 according to an embodiment 6 ofthe present invention. The risk information collection device 600illustrated in FIG. 19 includes a non-subject vehicle informationacquisition unit 112 acquiring risk-inducing factor informationregarding a non-subject vehicle in addition to the configuration of therisk information collection device 500 according to the embodiment 5illustrated in FIG. 13. In FIG. 19, the same reference numerals will beassigned to the same configuration as the risk information collectiondevice 500 described using FIG. 13, and a duplicate description isomitted.

As illustrated in FIG. 19, the information of the risk-inducing factorof the non-subject vehicle acquired in the non-subject vehicleinformation acquisition unit 112 is output to the risk-inducing factorspecifying unit 107. The risk-inducing factor specifying unit 107specifies the risk-inducing factor using the information of thevisually-recognized object group being output from thevisually-recognized object specifying unit 103, the information of therisk-inducing factor candidate group being output from the risk-inducingfactor candidate setting unit 106, the current position informationbeing output from the position information data acquisition unit 109,the past risk information being output from the risk informationdatabase 111, and the risk-inducing factor of the non-subject vehiclebeing output from the non-subject vehicle information acquisition unit112, and outputs the risk-inducing factor to the risk-inducing factoroutput unit 108.

Herein, a name of the risk-inducing factor specified in the non-subjectvehicle falls under the information of the risk-inducing factor of thenon-subject vehicle. The position information at the time of theoccurrence of the risk-inducing factor specified in the non-subjectvehicle may be added. The non-subject vehicle information acquisitionunit 112 may acquire the information of the risk-inducing factor of thenon-subject vehicle via a direct communication with a risk informationcollection device mounted on the non-subject vehicle, or may acquire theinformation via a cloud server. The risk-inducing factor specified inthe non-subject vehicle and being output from the non-subject vehicleinformation acquisition unit 112 and the position information thereofmay be accumulated in the risk information database 111 as the past riskinformation.

FIG. 20 is a flow chart describing an operation of the risk informationcollection device 600. Processing in Steps S71 to S75 is the same as theprocessing in Steps S51 to S55 in the flow chart illustrated in FIG. 10.Weighting processing in Step S77 for adding a numeral value to thedegree of the relationship based on the risk-inducing factor and theposition information which are past statistical information stored inthe risk information database 111 after the degree of relationshipbetween the visually-recognized object candidate group and therisk-inducing factor candidate is calculated and the degree ofrelationship is calculated as the numeral value in Step S76 in therisk-inducing factor specifying unit 107 is the same as that in theembodiment 5. However, further added is weighting processing in Step S78for adding a numeral value to the degree of relationship based on therisk-inducing factor of the non-subject vehicle being output from thenon-subject vehicle information acquisition unit 112.

The non-subject vehicle information acquisition unit 112 acquires therisk-inducing factor of the non-subject vehicle, which is located aroundthe subject vehicle, from the non-subject vehicle. The risk-inducingfactor specifying unit 107 checks the relationship between thevisually-recognized object candidate group and the risk-inducing factorcandidate group from the information of the visually-recognized objectgroup being output from the visually-recognized object specifying unit103 and the information of the risk-inducing factor candidate groupbeing output from the risk-inducing factor candidate setting unit 107(Step S76). After checking the relationship, the risk-inducing factorspecifying unit 107 searches the information stored in the riskinformation database 111 to confirm, based on the position informationbeing output from the position information data acquisition unit 109,whether or not there are position information at a point of time whenthe degree of risk calculated in the degree of risk calculation unit 102becomes equal to or larger than a threshold value and a risk-inducingfactor associated with an area around a position indicated by theposition information.

When there is the corresponding risk-inducing factor, informationthereof is acquired. The risk-inducing factor specifying unit 107 checksthe risk-inducing factor candidate against the past risk-inducing factorwhich has been acquired, and when the risk-inducing factor candidatecoincides with the past risk-inducing factor, a point is added to thedegree of relationship of the coinciding risk-inducing factor candidate(Step S77).

The risk-inducing factor specifying unit 107 further acquires therisk-inducing factor of the non-subject vehicle, which is located aroundthe subject vehicle, being output from the non-subject vehicleinformation acquisition unit 112. The risk-inducing factor specifyingunit 107 checks the risk-inducing factor candidate against therisk-inducing factor of the non-subject vehicle which has been acquired,and when the risk-inducing factor candidate coincides with therisk-inducing factor of the non-subject vehicle, a point is added to thedegree of relationship of the coinciding risk-inducing factor candidate(Step S78). For example, in a case where there is the word of “cat” inthe risk-inducing factor of the non-subject vehicle which has beenacquired, the point is added to the degree of relationship of “cat” inthe risk-inducing factor candidate when there is “cat” in therisk-inducing factor candidate. The range of the surrounding area of thesubject vehicle described above is specified within a radius centeringon the subject vehicle, for example, and the radius may be selected from300 m, 500 m, and 1 km.

The risk-inducing factor specifying unit 107 determines therisk-inducing factor candidate having the highest point of the degree ofrelationship, including the weighted degree of relationship, to be therisk-inducing factor (Step S79).

As described above, according to the risk information collection device600 of the embodiment 6 of the present invention, the object inducingthe risk can be specifically specified. Thus, the factor which hasinduced the risk can be specified in addition to the function ofspecifying the dangerous spot, thus the user can be provided with theinformation indicating the cause of the risk. The risk informationcollection device 600 includes the non-subject vehicle informationacquisition unit 112, and uses the information of the risk-inducingfactor of the non-subject vehicle located around the subject vehicle,thereby being able to further increase the accuracy for specifying therisk-inducing factor.

MODIFICATION EXAMPLE

The non-subject vehicle information acquisition unit 112 is added to therisk information collection devices 100, 200, 300, and 400 according tothe embodiments 1 to 4, thereby being able to increase the accuracy forspecifying the risk-inducing factor.

That is to say, a risk information collection device 100B illustrated inFIG. 21 has a configuration that not only the information of thevisually-recognized object group being output from thevisually-recognized object specifying unit 103 and the information ofthe risk-inducing factor candidate group being output from therisk-inducing factor candidate setting unit 106 but also information ofthe risk-inducing factor of the non-subject vehicle being output fromthe non-subject vehicle information acquisition unit 112 are input tothe risk-inducing factor specifying unit 107.

A risk information collection device 200B illustrated in FIG. 22 has aconfiguration that not only the information of the visually-recognizedobject group being output from the visually-recognized object specifyingunit 103 and the information of the risk-inducing factor candidate groupbeing output from the risk-inducing factor candidate setting unit 106but also information of the risk-inducing factor of the non-subjectvehicle being output from the non-subject vehicle informationacquisition unit 112 are input to the risk-inducing factor specifyingunit 107.

A risk information collection device 300B illustrated in FIG. 23 has aconfiguration that not only the information of the visually-recognizedobject group being output from the visually-recognized object specifyingunit 103 and the information of the risk-inducing factor candidate groupbeing output from the risk-inducing factor candidate setting unit 106but also information of the risk-inducing factor of the non-subjectvehicle being output from the non-subject vehicle informationacquisition unit 112 are input to the risk-inducing factor specifyingunit 107.

A risk information collection device 400B illustrated in FIG. 24 has aconfiguration that not only the information of the visually-recognizedobject group being output from the visually-recognized object specifyingunit 103 and the information of the risk-inducing factor candidate groupbeing output from the risk-inducing factor candidate setting unit 106but also information of the risk-inducing factor of the non-subjectvehicle being output from the non-subject vehicle informationacquisition unit 112 are input to the risk-inducing factor specifyingunit 107.

In FIG. 21 to FIG. 24, the same reference numerals will be assigned tothe same configuration as the risk information collection devices 100,200, 300, and 400 described using FIG. 1, FIG. 8, FIG. 11, and FIG. 12,and a duplicate description is omitted.

The main configurations of the risk information collection devices 100,200, 300, 400, 500, 600, 100A, 200A, 300A, 100B, 200B, and 300B can bemade up using a computer, and each configuration thereof is achievedwhen the computer executes a program. For example, the travelinformation acquisition unit 101, the degree of risk calculation unit102, the visually-recognized object specifying unit 103, the passengerinformation acquisition unit 104, the risk-inducing factor candidatesetting unit 106, the risk-inducing factor specifying unit 107, and therisk-inducing factor output unit 108 in the risk information collectiondevice 100 illustrated in FIG. 1 are achieved by a processing circuit100 illustrated in FIG. 25. A processor such as a central processingunit (CPU) and a digital signal processor (DSP) is applied to theprocessing circuit 10, and a function of each configuration describedabove is achieved by executing a program stored in a storage device.

Dedicated hardware may be applied to the processing circuit 10. When theprocessing circuit 10 is the dedicated hardware, a single circuit, acomplex circuit, a programmed processor, a parallel-programmedprocessor, an application specific integrated circuit (ASIC), a fieldprogrammable gate array (FPGA), or a circuit combining them, forexample, falls under the processing circuit 10.

FIG. 26 illustrates, as an example, a hardware configuration in a casewhere each configuration of the risk information collection device 100illustrated in FIG. 1 is made up using a processor. In this case, eachfunction of the configuration of the risk information collection device100 is achieved by a combination of software (software, firmware, or acombination of software and firmware), for example. The software, forexample, is described as a program and is stored in a memory 12. Aprocessor 11 functioning as the processing circuit 10 reads out andexecutes the program stored in the memory 12 (the storage device),thereby achieving the function of each unit.

The present invention has been shown and described in detail, theforegoing description is in all aspects illustrative and notrestrictive. It is therefore understood that numerous modifications andvariations can be devised without departing from the scope of theinvention.

According to the present invention, each embodiment can be arbitrarilycombined, or each embodiment can be appropriately varied or omittedwithin the scope of the invention.

1. A risk information collection device mounted on a vehicle andcollecting information dangerous to a traveling of the vehicle,comprising a processing circuit, wherein the processing circuit isconfigured to perform: travel information acquisition processing ofacquiring travel information indicating a travel state of the vehicle;degree of risk calculation processing of calculating a degree of riskwhich is to be an index whether a travel state of the vehicle is indanger based on the travel information acquired in the travelinformation acquisition processing; visually-recognized objectspecifying processing of combining visual line information of a driverof the vehicle and object information around the vehicle and specifyinga visually-recognized object of the driver to set a visually-recognizedobject candidate; passenger information acquisition processing ofacquiring passenger information including words pronounced by apassenger; risk-inducing factor candidate setting processing of settingat least one risk-inducing factor candidate for specifying arisk-inducing factor which has induced a risk based on the passengerinformation acquired in the passenger information acquisitionprocessing; and risk-inducing factor specifying processing of specifyingthe risk-inducing factor based on the degree of risk calculated in thedegree of risk calculation processing, the visually-recognized objectcandidate specified in the visually-recognized object specifyingprocessing, and the risk-inducing factor candidate being set in therisk-inducing factor candidate setting processing the risk-inducingfactor specifying processing checks a relationship between thevisually-recognized object candidate and the risk-inducing factorcandidate when the degree of risk becomes equal to or larger than athreshold value, and specifies the risk-inducing factor candidate as therisk-inducing factor when the visually-recognized object candidate andthe risk-inducing factor candidate are associated with each other, andthe risk-inducing factor candidate setting processing performs voiceanalysis on words pronounced by the passenger, divides the words intosingle words, and sets a plurality of the words, which have beendivided, as the risk-inducing factor candidate in one set.
 2. (canceled)3. The risk information collection device according to claim 1, whereinthe passenger information includes video information of a behavior ofthe passenger, and the risk-inducing factor candidate setting processingperforms video analysis on the behavior of the passenger, and when thebehavior of the passenger is a behavior of specifying an object outsidethe vehicle, the risk-inducing factor candidate setting processingspecifies the object and adds a word of a name of the object to therisk-inducing factor candidate.
 4. The risk information collectiondevice according to claim 1, wherein the travel information includesinformation of a speed, a presence or absence of a braking operation, anacceleration, and a steering angle, and the degree of risk calculationprocessing sets the degree of risk by a numeral value in multiple stagesby combination of the information of the speed, the presence or absenceof the braking operation, the acceleration, and the steering angle. 5.The risk information collection device according to claim 3, wherein inchecking a relationship between the visually-recognized object candidateand the risk-inducing factor candidate, when a word expressing an objector a word expressing a state of the object in the risk-inducing factorcandidate coincides with the visually-recognized object candidate, therisk-inducing factor specifying processing links the visually-recognizedobject candidate with the risk-inducing factor candidate, and specifiesthe risk-inducing factor candidate as the risk-inducing factor.
 6. Therisk information collection device according to claim 3, wherein inchecking a relationship between the visually-recognized object candidateand the risk-inducing factor candidate, when a word expressing an objector a word expressing a state of the object in the risk-inducing factorcandidate coincides with the visually-recognized object candidate, therisk-inducing factor specifying processing links the visually-recognizedobject candidate with the risk-inducing factor candidate, and when thereare a plurality of the risk-inducing factor candidates, therisk-inducing factor specifying processing classifies each word in therisk-inducing factor candidates into a word expressing the object, aword expressing a state of the object, and a word expressing a degree ofrisk, assigns a predetermined point to the each word for eachclassification as an index of a degree of relationship, calculates a sumof the predetermined point for each of the risk-inducing factorcandidates, and specifies the risk-inducing factor candidate having thehighest sum of the predetermined point as the risk-inducing factor. 7.The risk information collection device according to claim 3, furthercomprising a risk association information database storing informationassociated with a risk, wherein the risk-inducing factor candidatesetting processing refines the passenger information using theinformation associated with the risk in setting the risk-inducing factorcandidate, the information associated with the risk includes at least aword associated with a risk, and in refining the passenger information,when a plurality of words in the risk-inducing factor candidate includethe word associated with the risk in the risk association informationdatabase, the risk-inducing factor candidate setting processing sets theword associated with the risk in the risk-inducing factor candidate andwords pronounced before and after the word associated with the risk asthe risk-inducing factor candidate.
 8. (canceled)
 9. (canceled)
 10. Therisk information collection device according to claim 3, furthercomprising a risk information database storing a past risk-inducingfactor and position information of the past risk-inducing factor,wherein the processing circuit performs position information dataacquisition processing of acquiring position information of the vehicle,and in checking a relationship between the visually-recognized objectcandidate and the risk-inducing factor candidate, when the riskinformation database includes the past risk-inducing factor associatedwith the position information of the vehicle being output in theposition information data acquisition processing at a point of time whenthe degree of risk becomes equal to or larger than a threshold value andthe past risk-inducing factor and the risk-inducing factor candidatecoincide with each other, the risk-inducing factor specifying processingperforms weighting processing on a degree of relationship of therisk-inducing factor candidate coinciding with the past risk-inducingfactor.
 11. The risk information collection device according to claim10, wherein the processing circuit performs non-subject vehicleinformation acquisition processing of acquiring a risk-inducing factorof a non-subject vehicle, and in checking a relationship between thevisually-recognized object candidate and the risk-inducing factorcandidate, when the risk-inducing factor of the non-subject vehicleacquired in the non-subject vehicle information acquisition processingassociated with the position information of the vehicle being output inthe position information data acquisition processing at a point of timewhen the degree of risk becomes equal to or larger than a thresholdvalue and the risk-inducing factor candidate coincide with each other,the risk-inducing factor specifying processing performs weightingprocessing on a degree of relationship of the risk-inducing factorcandidate coinciding with the risk-inducing factor.
 12. A riskinformation collection device mounted on a vehicle and collectinginformation dangerous to a traveling of the vehicle, comprising: aprocessing circuit, wherein the processing circuit is configured toperform: travel information acquisition processing of acquiring travelinformation indicating a travel state of the vehicle; degree of riskcalculation processing of calculating a degree of risk which is to be anindex whether a travel state of the vehicle is in danger based on thetravel information acquired in the travel information acquisitionprocessing; visually-recognized object specifying processing ofcombining visual line information of a driver of the vehicle and objectinformation around the vehicle and specifying a visually-recognizedobject of the driver to set a visually-recognized object candidate;passenger information acquisition processing of acquiring passengerinformation including at least words pronounced by a passenger;risk-inducing factor candidate setting processing of setting at leastone risk-inducing factor candidate for specifying a risk-inducing factorwhich has induced a risk based on the passenger information acquired inthe passenger information acquisition processing; risk-inducing factorspecifying processing of specifying a risk-inducing factor based on thedegree of risk calculated in the degree of risk calculation processing,the visually-recognized object candidate specified in thevisually-recognized object specifying processing, and the risk-inducingfactor candidate being set in the risk-inducing factor candidate settingprocessing; and position information data acquisition processing ofacquiring position information of the vehicle, wherein the risk-inducingfactor specifying processing checks a relationship between thevisually-recognized object candidate and the risk-inducing factorcandidate when the degree of risk becomes equal to or larger than athreshold value, and specifies the risk-inducing factor candidate as therisk-inducing factor when the visually-recognized object candidate andthe risk-inducing factor candidate are associated with each other, andoutputs position information of the vehicle acquired in the positioninformation data acquisition processing at a point of time when thetravel state of the vehicle being in danger has occurred, together withthe risk-inducing factor which has been specified.
 13. A riskinformation collection device mounted on a vehicle and collectinginformation dangerous to a traveling of the vehicle, comprising aprocessing circuit, wherein the processing circuit is configured toperform: travel information acquisition processing of acquiring travelinformation indicating a travel state of the vehicle; degree of riskcalculation processing of calculating a degree of risk which is to be anindex whether a travel state of the vehicle is in danger based on thetravel information acquired in the travel information acquisitionprocessing; visually-recognized object specifying processing ofcombining visual line information of a driver of the vehicle and objectinformation around the vehicle and specifying a visually-recognizedobject of the driver to set a visually-recognized object candidate;passenger information acquisition processing of acquiring passengerinformation including at least words pronounced by a passenger;risk-inducing factor candidate setting processing of setting at leastone risk-inducing factor candidate for specifying a risk-inducing factorwhich has induced a risk based on the passenger information acquired inthe passenger information acquisition processing; risk-inducing factorspecifying processing of specifying a risk-inducing factor based on thedegree of risk calculated in the degree of risk calculation processing,the visually-recognized object candidate specified in thevisually-recognized object specifying processing, and the risk-inducingfactor candidate being set in the risk-inducing factor candidate settingprocessing; and surround information collection processing of collectingsurround information including a congestion degree of vehicle and acongestion degree of people around the vehicle, wherein therisk-inducing factor specifying processing checks a relationship betweenthe visually-recognized object candidate and the risk-inducing factorcandidate when the degree of risk becomes equal to or larger than athreshold value, and specifies the risk-inducing factor candidate as therisk-inducing factor when the visually-recognized object candidate andthe risk-inducing factor candidate are associated with each other, andoutputs the surround information being acquired in the surroundinformation collection processing in a position where the travel stateof the vehicle being in danger has occurred, together with therisk-inducing factor which has been specified.